Abstract [en]

Delays and cancelled trains are often described as a problem for companies that transport goods by rail. Lack of reliability of freight trains is seen as a disadvantage compared to other modes. To date there has been limited quantitative information in Sweden about the size and the structure of the problem of trains not arriving in time.

In this thesis information for two years, 2008 and 2009 is available that enable us to do the analysis of distribution on a spatial, temporal and seize-frequency scale. Since the spatial and frequency-size distributions describe the vulnerability of a transport network it has potentially important policy implications.

In the size-frequency scale, we analyzed different distribution to fit with the arrival delay at the final station and we conclude that, it is not possible to find a distribution for the whole range of observations. However, considering the tail of arrival delays we find that it is exponentially distributed. This implies that the tail makes up the biggest part of total delay time. The 20 % largest delays contribute to about 78% of total delay minutes.

In the spatial scale, we defined stations which have the highest value of arrival delay in the whole network and ranked them. We found out that more than 50% of the total arrival delay per year occurs in just 7% of stations. With the help of regression analysis we analyze how delays are propagated in the network. We find that delays at the origin increase arrival delay but that some part of the initial delay is gained at arrival, probably due to large slack in the time tables.

Finally, in the temporal scale we analyzed arrival delays in different time scales such as monthly, weekly and daily delays. We expected that the reduction of the total number of trains in 2009 would reduce not only total but also the average arrival delay since there would be more free capacity. The data shows however, that the average delay did not decrease as the number of trains decreased due to the economic contraction in 2009, indicating that capacity might not be as crucial for explaining delays as previously expected.